--- license: mit datasets: - CodeGoat24/HPD - CodeGoat24/LiFT-HRA - CodeGoat24/OIP - CodeGoat24/EvalMuse - CodeGoat24/ShareGPTVideo-DPO - CodeGoat24/VideoFeedback - CodeGoat24/LLaVA-Critic-113k - CodeGoat24/VideoDPO base_model: - lmms-lab/llava-onevision-qwen2-7b-ov --- # Unified-Reward-7B We are actively gathering feedback from the community to improve our models. **We welcome your input and encourage you to stay updated through our repository**!! [2025/4/15] 🔥🔥 We updated the `UnifiedReward-7B` to enhance its generalization and performance, incorporating valuable feedback from the community. ## Model Summary `Unified-Reward-7b` is the first unified reward model for multimodal understanding and generation assessment, enabling both pairwise ranking and pointwise scoring, which can be employed for vision model preference alignment. For further details, please refer to the following resources: - 📰 Paper: https://arxiv.org/pdf/2503.05236 - 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/ - 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a - 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede - 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io) # 🔥 News [2025/10/23] 🔥🔥🔥 We release **UnifiedReward-Edit**-[[3b](https://huggingface.co/CodeGoat24/UnifiedReward-Edit-qwen-3b)/[7b](https://huggingface.co/CodeGoat24/UnifiedReward-Edit-qwen-7b)/[32b](https://huggingface.co/CodeGoat24/UnifiedReward-Edit-qwen-32b)/[72b](https://huggingface.co/CodeGoat24/UnifiedReward-Edit-qwen-72b)], a unified reward model for **both Text-to-Image and Image-to-Image generation** trained on approximately 700K unified image generation and editing reward data!! For image editing reward task, our models support: >1. Pairwise Rank — directly judge which of two edited images is better. > >2. Pairwise Score — assign a separate score to each image in a pair. > >3. Pointwise Score — rate a single image on two axes: instruction-following and overall image quality. 🚀 The image editing reward inference code is available at [`UnifiedReward-Edit/`](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Edit) directory, while T2I inference code is unchanged from previous models. The editing training data is preprocessed from [EditScore](https://huggingface.co/datasets/EditScore/EditScore-Reward-Data) and [EditReward](https://huggingface.co/datasets/TIGER-Lab/EditReward-Data) and will be released soon. We sincerely appreciate all contributors!! [2025/9/25] 🔥🔥🔥 We release **UnifiedReward-2.0**-qwen-[[3b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-3b)/[7b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-7b)/[32b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-32b)/[72b](https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-72b)]. This version introduces several new capabilities: > >1. **Pairwise scoring** for image and video generation assessment on **_Alignment_**, **_Coherence_**, **_Style_** dimensions. > >2. **Pointwise scoring** for image and video generation assessment on **_Alignment_**, **_Coherence/Physics_**, **_Style_** dimensions. > The added inference code is available at [`inference_qwen/UnifiedReward-2.0-inference`](https://github.com/CodeGoat24/UnifiedReward/tree/main/inference_qwen/UnifiedReward-2.0-inference) directory. The newly added training data has been released [here](https://huggingface.co/datasets/CodeGoat24/UnifiedReward-2.0-T2X-score-data) 😊. ## 🏁 Compared with Current Reward Models | Reward Model | Method| Image Generation | Image Understanding | Video Generation | Video Understanding | :-----: | :-----: |:-----: |:-----: | :-----: | :-----: | | [PickScore](https://github.com/yuvalkirstain/PickScore) |Point | √ | | || | [HPS](https://github.com/tgxs002/HPSv2) | Point | √ | ||| | [ImageReward](https://github.com/THUDM/ImageReward) | Point| √| ||| | [LLaVA-Critic](https://huggingface.co/lmms-lab/llava-critic-7b) | Pair/Point | | √ ||| | [IXC-2.5-Reward](https://github.com/InternLM/InternLM-XComposer) | Pair/Point | | √ ||√| | [VideoScore](https://github.com/TIGER-AI-Lab/VideoScore) | Point | | |√ || | [LiFT](https://github.com/CodeGoat24/LiFT) | Point | | |√| | | [VisionReward](https://github.com/THUDM/VisionReward) | Point |√ | |√|| | [VideoReward](https://github.com/KwaiVGI/VideoAlign) | Point | | |√ || | UnifiedReward (Ours) | Pair/Point | √ | √ |√|√| ### Quick Start All pair rank and point score inference codes are provided in our [github](https://github.com/CodeGoat24/UnifiedReward). We take image understanding assessment as example here: ~~~python # pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git from llava.model.builder import load_pretrained_model from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX from llava.conversation import conv_templates, SeparatorStyle from PIL import Image import requests import copy import torch import sys import warnings import os warnings.filterwarnings("ignore") pretrained = "CodeGoat24/UnifiedReward-7b" model_name = "llava_qwen" device = "cuda" device_map = "auto" tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map) # Add any other thing you want to pass in llava_model_args model.eval() url = "https://github.com/LLaVA-VL/blog/blob/main/2024-10-03-llava-critic/static/images/critic_img_seven.png?raw=True" image = Image.open(requests.get(url, stream=True).raw) image_tensor = process_images([image], image_processor, model.config) image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor] conv_template = "qwen_1_5" # Make sure you use correct chat template for different models # pairwise ranking critic_prompt = "Given an image and a corresponding question, please serve as an unbiased and fair judge to evaluate the quality of the answers provided by a Large Multimodal Model (LMM). Determine which answer is better and explain your reasoning with specific details. Your task is provided as follows:\nQuestion: [What this image presents?]\nThe first response: [The image is a black and white sketch of a line that appears to be in the shape of a cross. The line is a simple and straightforward representation of the cross shape, with two straight lines intersecting at a point.]\nThe second response: [This is a handwritten number seven.]\nASSISTANT:\n" # pointwise scoring # critic_prompt = "Given an image and a corresponding question, please serve as an unbiased and fair judge to evaluate the quality of answer answers provided by a Large Multimodal Model (LMM). Score the response out of 100 and explain your reasoning with specific details. Your task is provided as follows:\nQuestion: [What this image presents?]\nThe LMM response: [This is a handwritten number seven.]\nASSISTANT:\n " question = DEFAULT_IMAGE_TOKEN + "\n" + critic_prompt conv = copy.deepcopy(conv_templates[conv_template]) conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt_question = conv.get_prompt() input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device) image_sizes = [image.size] cont = model.generate( input_ids, images=image_tensor, image_sizes=image_sizes, do_sample=False, temperature=0, max_new_tokens=4096, ) text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True) print(text_outputs[0]) ~~~ ## Citation ``` @article{unifiedreward, title={Unified reward model for multimodal understanding and generation}, author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi}, journal={arXiv preprint arXiv:2503.05236}, year={2025} } ```